Computer vision technology provides a powerful tool for human-machines interfaces. There are many applications that can benefit from a computer determination of human eye position and/or movements. One application, for example, is an automobile that can determine whether the driver's eyes are open and looking at the road. If the driver has fallen asleep, the automobile computer can act appropriately to restore a safe operating environment.
One conventional approach to detecting eye movements uses methods that are intrusive upon the human subject. Intrusive methods include, for example, using a chin support, a head-mounted camera, or other special devices to constrain face positioning with respect to a sensor or camera. One problem with intrusive methods is user acceptance. Users typically dislike applying an unnatural or unwelcome device in order to interface with the computer.
Other conventional approaches use non-intrusive techniques. Typical non-intrusive eye detection and tracking techniques can be classified into two mutually exclusive categories: active infrared (IR) illumination methods and appearance-based methods. An active IR technique illuminates a subject's face using an IR emitter such as a light emitting diode (LED). In certain external lighting conditions, the eye's pupil can appear brighter than the rest of the face. The active IR method uses differential IR illumination to detect the high contrast between the pupils and the rest of the face.
One problem with this technique is that its accuracy depends on the brightness and size of the pupils, which is often a function of face orientations, external illumination interferences, and the distance of the subject to the camera. Another problem with this technique is that the subject needs to be close to camera because different face orientations and distance make it more difficult to get a good differential image of the pupils. The robustness of the active IR approach, therefore, depends upon the stability of the lighting conditions and close proximity of the subject to the camera.
A typical appearance-based method detects a subject's eyes based on the intensity (or color) distribution of the eyes, which appear different from the rest of the face. Eyes can be detected and tracked based on exploiting the differences in appearance. This method usually needs to collect a large amount of training data representing the eyes of different subjects, under different face orientations, and different illumination conditions. The conventional appearance-based approach, while not requiring special illumination, can require a significant amount of training data to enumerate all possible appearances of eyes because the eye's appearance can change dramatically due to different illuminations, face orientations, or the subject's eyeglasses.
What is needed is a technique for detecting and tracking eye movements that is non-intrusive and acceptable to a user. What is further needed is a technique for detecting and tracking eye movements that is robust under various light conditions and subject positions.